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WassersteinTSNE-1.1.1
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تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A package for dimensionality reduction of probability distributions
| ویژگی | مقدار |
|---|---|
| سیستم عامل | POSIX :: Linux |
| نام فایل | WassersteinTSNE-1.1.1 |
| نام | WassersteinTSNE |
| نسخه کتابخانه | 1.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Fynn Bachmann, Philipp Hennig, Dmitry Kobak |
| ایمیل نویسنده | fynn.bachmann@uni-hamburg.de |
| آدرس صفحه اصلی | https://github.com/fsvbach/WassersteinTSNE |
| آدرس اینترنتی | https://pypi.org/project/WassersteinTSNE/ |
| مجوز | MIT |
# WassersteinTSNE
This package provides the methods described in the _Wasserstein t-SNE_ paper on [arXiv.org](http://arxiv.org/abs/2205.07531).
To reproduce the figures in that paper, please also check the repository [wassersteinTSNE-paper](https://github.com/fsvbach/wassersteinTSNE-paper).
## Installation
You can install WassersteinTSNE via
```
pip install WassersteinTSNE
```
or clone this repository into your working directory.
## Basic Usage
You may import the package in either of these ways
```
import WassersteinTSNE as WT
from WassesteinTSNE import TSNE
```
### Data
The data should be provided in either of two ways:
1. As a `pd.DataFrame` where the index indicates which sample belongs to which units
2. As a `np.ndarray` where each line corresponds to a sample **and** a list of unit ids
If you don't have a dataset at hand you can generate a toy dataset by running
```
dataset, HGM = WT.ToyDataset()
```
or create a random HGMM
```
HGM = WT.HierarchicalGaussianMixture(seed=67)
dataset = HGM.generate_data()
```
By default that creates a HGMM with K=4 classes. This corresponds to a `pd.DataFrame` with N=100 units and M=30 samples each. If each sample has F=2 (as in this example) features, you can visualize the generated HGMM by
```
WT.plotMixture(HGM)
```
### Gaussian Wasserstein t-SNE
The straight forward way to embed your hierarchical dataset is
```
embedding = WT.TSNE(dataset, seed=67, w=0.5)
```
or do the procedure step by step with
```
Gaussians = WT.Dataset2Gaussians(dataset)
GWD = WT.GaussianWassersteinDistance(Gaussians)
embedding = WT.ComputeTSNE(GWD.matrix(w=0.5), seed=67)
```
This is built upon [openTSNE](https://github.com/pavlin-policar/openTSNE) with the addition, that all embeddings are returned as a `pd.DataFrame`. These can be visualized with
```
WT.embedScatter(embedding, title='DemoEmbedding')
```
If you have defined classes, you can pass a dictionary that maps the unit ids to their class
```
WT.embedScatter(embedding, labeldict=HGM.labeldict())
```
to color the units according to their class.
By adjusting the hyperparameter `w` you can put emphasis on the means or covariance matrices of the units. With
```
D = GWD.matrix(w=0.7)
```
you can obtain the distance matrix for any value of `w`. To visualize a range of matrices you may call
```
WT.plotMatrices([GWD.matrix(w=w) for w in WT.naming.keys()], WT.naming.values())
```
## Exact Wasserstein Distances
It is possible to compute the exact Wasserstein distances of a dataset as well. Depending on the number of units this can take some time. However, for the dataset in `WT.ToyDataset()` the computation of the pairwise distance matrix should take less than 8min on a desktop computer by running
```
D = WT.WassersteinDistanceMatrix(dataset)
```
This yields the NxN distance matrix as a `pd.DataFrame` which can then be embedded with
```
embedding = WT.ComputeTSNE(D)
```
A shortcut for this procedure is provided with
```
embedding = WT.TSNE(dataset, method='exact')
```
## Evaluation
We implemented two methods to evaluate the distance matrix of a hierarchical dataset. For both of them it is necessary to have the ground truth available as a `dict()` or as a list of labels.
```
labels = HGM.labeldict()
```
### kNN Accuracy
The kNN accuracy computes the kNN graph of the t-SNE embedding and labels each point by the majority vote of its k nearest neighbors. Using the true labels, the accuracy is then computed with
```
WT.knnAccuracy(embedding, labels)
```
### Leiden clustering
A t-SNE independent method is provided by the Leiden algorithm, that runs directly on the distance matrix.
```
WT.LeidenClusters(D, labels)
```
نحوه نصب
نصب پکیج whl WassersteinTSNE-1.1.1:
pip install WassersteinTSNE-1.1.1.whl
نصب پکیج tar.gz WassersteinTSNE-1.1.1:
pip install WassersteinTSNE-1.1.1.tar.gz